571 research outputs found

    The Feasibility and Satisfaction of Using Telemedicine to Provide Tertiary Paediatric Obesity Care

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    Although telemedicine in paediatric tertiary care has been tried, satisfaction and outcome data is limited due to small sample sizes and the variation of methods and personnel by which telemedicine technology is delivered and participants assessed. Aim: to determine the feasibility of using telemedicine to deliver Paediatric tertiary obesity care and to evaluate Patient/Physician/Staff satisfaction. Method: we used a commercially available telemedicine system to conduct a prospective study where 30 patients in a Paediatric Healthy Life Centre Outpatient Clinic were evaluated by a specialist physician using telemedicine technology. A qualitative assessment of the patient/physician/staff perceptions of telemedicine use was assessed through a five-point Likert Scale and free text answers. Results: of the 30 patient sessions, 27 (90%) consultations were performed to completion. Fifteen consultations were initial assessments. Physician, staff and patient responders agreed that the use of telemedicine is an appropriate and effective use of the clinician’s skillset and time (≥96%), and can avoid patient travel from an underserved area to a tertiary care clinic (≥95%). Responders were comfortable and satisfied using the telemedicine equipment (≥85%). Physician and patients agreed that the telemedicine equipment helped the patient avoid a face-to-face visit (≥90%). Physician and patients felt the technology was effective in the management of their visit (≥93%). Conclusions: we successfully delivered tertiary obesity care through the use of telemedicine equipment in different clinical situations associated with paediatric obesity. Telemedicine is feasible, and is a realistic, successful and cost-effective modality to provide well-received specialty care for the obese paediatric population

    Note sur les crustacés isopodes de Belgique

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    BatchQueue : file producteur / consommateur optimisée pour les multi-cœurs

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    National audienceLes applications séquentielles peuvent tirer partie des systèmes multi-cœurs en utilisant le parallélisme pipeline pour accroître leur performance. Dans un tel schéma de parallélisme, l'accélération possible est limitée par le surcoût dû à la communication cœur à cœur. Ce papier présente l'algorithme BatchQueue, un système de communication rapide conçu pour optimiser l'utilisation du cache matériel, notamment au regard du pré-chargement. BatchQueue propose des performances améliorées d'un facteur 2 : il est capable d'envoyer un mot de données en 3,5 nanosecondes sur un système 64 bits, représentant un débit de 2 Gio/s

    Clinical Determinants and Prognostic Implications of Renin and Aldosterone in Patients with Symptomatic Heart Failure

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    Aims Activation of the renin-angiotensin-aldosterone system plays an important role in the pathophysiology of heart failure (HF) and has been associated with poor prognosis. There are limited data on the associations of renin and aldosterone levels with clinical profiles, treatment response, and study outcomes in patients with HF. Methods and results We analysed 2,039 patients with available baseline renin and aldosterone levels in BIOSTAT-CHF (a systems BIOlogy study to Tailored Treatment in Chronic Heart Failure). The primary outcome was the composite of all-cause mortality or HF hospitalization. We also investigated changes in renin and aldosterone levels after administration of mineralocorticoid receptor antagonists (MRAs) in a subset of the EPHESUS trial and in an acute HF cohort (PORTO). In BIOSTAT-CHF study, median renin and aldosterone levels were 85.3 (percentile(25-75) = 28-247) mu IU/mL and 9.4 (percentile(25-75) = 4.4-19.8) ng/dL, respectively. Prior HF admission, lower blood pressure, sodium, poorer renal function, and MRA treatment were associated with higher renin and aldosterone. Higher renin was associated with an increased rate of the primary outcome [highest vs. lowest renin tertile: adjusted-HR (95% CI) = 1.47 (1.16-1.86), P = 0.002], whereas higher aldosterone was not [highest vs. lowest aldosterone tertile: adjusted-HR (95% CI) = 1.16 (0.93-1.44), P = 0.19]. Renin and/or aldosterone did not improve the BIOSTAT-CHF prognostic models. The rise in aldosterone with the use of MRAs was observed in EPHESUS and PORTO studies. Conclusions Circulating levels of renin and aldosterone were associated with both the disease severity and use of MRAs. By reflecting both the disease and its treatments, the prognostic discrimination of these biomarkers was poor. Our data suggest that the "point" measurement of renin and aldosterone in HF is of limited clinical utility

    Student model in intelligent tutor systems

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    Cilj diplomskega dela je teoretično raziskovanje modela učenca v inteligentnih tutorskih sistemih (ITS) in praktična realizacija konceptov na operacijskem sistemu Android, glede na hitro rast mobilneg segmenta in pomanjkanje ITS-jev na mobilnem področju. Predstavljeni so različni pristopi izdelave modela učenca in učnih stilov. Drugi del naloge predstavlja implementacija preprostega ITS-ja, kjer je model učenca zgrajen s pomočjo mehke logike, stereotipov in prekrivnega modela. Učno domeno v našem primeru predstavljajo osnovne operacije z ulomki. Aplikacija podaja personalizirano vsebino glede na trenutno stanje modela učenca. Učencu prav tako nudi personalizirane pomoči in vrača povratne informacije za optimalnejše učenje.The aim of this thesis is a theoretical research of the student model in intelligent tutoring systems (ITS) and a practical realisation of the concepts on Android OS, due to the rapid growth of the mobile segment and a lack of ITS in the mobile field. Various approaches in creating a student model and learning styles are presented. The second part of the thesis introduces the implementation of a simple ITS, where the student model is built using fuzzy logic, stereotypes and an overlay model. In the present case, the learning domain is represented by basic operations with fractions. The application provides personalised content based on the current state of the learner model. In addition, it offers personalised assistance, and a feedback for optimal learning
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